VidCRAFT3: Camera, Object, and Lighting Control for Image-to-Video Generation

Recent image-to-video generation methods have demonstrated success in enabling control over one or two visual elements, such as camera motion or object motion. However, these methods are unable to offer control over multiple visual elements due to limitations in data and network efficacy. In this paper, we introduce VidCRAFT3, a novel framework for precise image-to-video generation that enables control over camera motion, object motion, and lighting direction simultaneously. VidCRAFT3 integrates three core components: Image2Cloud generates 3D point cloud from a reference image; ObjMotionNet encodes sparse object trajectories using multi-scale optical flow features; and Spatial Triple-Attention Transformer incorporates lighting direction embeddings via parallel cross-attention modules. Additionally, we introduce the VideoLightingDirection dataset, providing synthetic yet realistic video clips with accurate per-frame lighting direction annotations, effectively mitigating the lack of annotated real-world datasets. We further adopt a three-stage training strategy, ensuring robust learning even without joint multi-element annotations. Extensive experiments show that VidCRAFT3 produces high-quality video content, outperforming state-of-the-art methods in control granularity and visual coherence. Code and data will be publicly available.